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        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

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        About MultiQC

        This report was generated using MultiQC, version 1.25.1

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

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        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        This report has been generated by the nf-core/rnaseq analysis pipeline. For information about how to interpret these results, please see the documentation.

        Report generated on 2024-11-06, 18:06 NZDT based on data in: /powerplant/workspace/cfnsjm/my_files/C_auratus/transcriptomics/rnaseq/work/10/c31b81bf6fd65b95976c8516ba1a89


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        General Statistics

        Showing 0/105 rows and 19/34 columns.
        Sample NamedupIntDuplication5'-3' biasM AlignedProper PairsError rateNon-primaryReads mapped% Mapped% Proper pairs% MapQ 0 readsTotal seqsReadsReads mapped% Reads mappedTotal readsAlignedAlignedUniq alignedUniq alignedMultimappedDupsGCAvg lenMedian lenFailedSeqsTrimmed basesDupsGCAvg lenMedian lenFailedSeqs
        101_F3
        0.1
        47.1%
        1.00
        22.5M
        47.4%
        0.58%
        4.0M
        45.1M
        100.0%
        100.0%
        0.3%
        45.1M
        49.1M
        49.1M
        100.0%
        29.6M
        22.6M
        76.2%
        21.3M
        72.0%
        1.2M
        101_F3_1
        58.4%
        48.0%
        150bp
        150bp
        45%
        29.6M
        5.2%
        69.6%
        48.0%
        140bp
        147bp
        27%
        29.6M
        101_F3_2
        54.3%
        49.0%
        150bp
        150bp
        36%
        29.6M
        5.3%
        64.5%
        49.0%
        140bp
        147bp
        9%
        29.6M
        114_F4
        0.1
        49.6%
        0.99
        17.2M
        44.4%
        0.51%
        3.6M
        34.4M
        100.0%
        100.0%
        0.4%
        34.4M
        37.9M
        37.9M
        100.0%
        23.0M
        17.2M
        74.8%
        16.1M
        70.1%
        1.1M
        114_F4_1
        59.9%
        48.0%
        150bp
        150bp
        36%
        23.0M
        5.1%
        69.9%
        48.0%
        140bp
        147bp
        18%
        23.0M
        114_F4_2
        56.2%
        48.0%
        150bp
        150bp
        36%
        23.0M
        5.2%
        66.1%
        48.0%
        140bp
        147bp
        18%
        23.0M
        117_F3
        0.1
        49.8%
        0.99
        15.9M
        43.3%
        0.53%
        4.1M
        31.8M
        100.0%
        100.0%
        0.7%
        31.8M
        35.8M
        35.8M
        100.0%
        20.9M
        15.9M
        76.0%
        14.7M
        70.5%
        1.2M
        117_F3_1
        60.5%
        47.0%
        150bp
        150bp
        36%
        20.9M
        5.9%
        69.9%
        47.0%
        139bp
        147bp
        18%
        20.9M
        117_F3_2
        57.3%
        48.0%
        150bp
        150bp
        36%
        20.9M
        6.0%
        65.9%
        48.0%
        139bp
        147bp
        18%
        20.9M
        119_F3
        0.1
        42.3%
        0.98
        12.3M
        52.8%
        0.58%
        1.6M
        24.6M
        100.0%
        100.0%
        0.3%
        24.6M
        26.2M
        26.2M
        100.0%
        15.9M
        12.3M
        77.2%
        11.8M
        74.2%
        0.5M
        119_F3_1
        53.0%
        48.0%
        150bp
        150bp
        36%
        15.9M
        5.8%
        63.6%
        48.0%
        139bp
        147bp
        27%
        15.9M
        119_F3_2
        48.7%
        48.0%
        150bp
        150bp
        27%
        15.9M
        5.8%
        59.6%
        48.0%
        139bp
        147bp
        18%
        15.9M
        123_F4
        0.1
        39.7%
        0.98
        16.3M
        55.1%
        0.59%
        2.2M
        32.7M
        100.0%
        100.0%
        0.2%
        32.7M
        34.8M
        34.8M
        100.0%
        20.7M
        16.3M
        78.9%
        15.6M
        75.6%
        0.7M
        123_F4_1
        51.1%
        49.0%
        150bp
        150bp
        45%
        20.7M
        5.8%
        62.8%
        49.0%
        139bp
        147bp
        27%
        20.7M
        123_F4_2
        48.3%
        49.0%
        150bp
        150bp
        27%
        20.7M
        5.8%
        58.3%
        49.0%
        139bp
        147bp
        18%
        20.7M
        124_F4
        0.1
        40.7%
        1.00
        19.4M
        55.2%
        0.60%
        1.9M
        38.8M
        100.0%
        100.0%
        0.1%
        38.8M
        40.6M
        40.6M
        100.0%
        23.2M
        19.4M
        83.4%
        18.8M
        80.8%
        0.6M
        124_F4_1
        52.5%
        48.0%
        150bp
        150bp
        45%
        23.3M
        5.7%
        64.1%
        48.0%
        139bp
        147bp
        27%
        23.2M
        124_F4_2
        47.8%
        49.0%
        150bp
        150bp
        27%
        23.3M
        5.8%
        60.0%
        49.0%
        139bp
        147bp
        18%
        23.2M
        131_M4
        0.1
        43.7%
        0.98
        23.2M
        50.3%
        0.58%
        4.3M
        46.4M
        100.0%
        100.0%
        0.4%
        46.4M
        50.7M
        50.7M
        100.0%
        28.9M
        23.2M
        80.5%
        22.1M
        76.7%
        1.1M
        131_M4_1
        55.5%
        49.0%
        150bp
        150bp
        45%
        28.9M
        4.5%
        67.0%
        49.0%
        141bp
        147bp
        27%
        28.9M
        131_M4_2
        51.3%
        50.0%
        150bp
        150bp
        36%
        28.9M
        4.6%
        62.9%
        50.0%
        141bp
        147bp
        9%
        28.9M
        134_F5
        0.1
        44.4%
        1.00
        22.1M
        49.8%
        0.61%
        3.9M
        44.2M
        100.0%
        100.0%
        0.4%
        44.2M
        48.1M
        48.1M
        100.0%
        28.7M
        22.1M
        77.1%
        20.9M
        72.9%
        1.2M
        134_F5_1
        56.5%
        49.0%
        150bp
        150bp
        45%
        28.7M
        4.7%
        67.9%
        49.0%
        141bp
        147bp
        27%
        28.7M
        134_F5_2
        52.6%
        49.0%
        150bp
        150bp
        45%
        28.7M
        4.8%
        63.6%
        49.0%
        141bp
        147bp
        18%
        28.7M
        135_M4
        0.1
        39.8%
        0.99
        15.5M
        54.2%
        0.62%
        2.6M
        31.0M
        100.0%
        100.0%
        0.3%
        31.0M
        33.6M
        33.6M
        100.0%
        19.7M
        15.5M
        78.8%
        14.7M
        74.9%
        0.8M
        135_M4_1
        51.2%
        49.0%
        150bp
        150bp
        45%
        19.7M
        5.5%
        61.4%
        48.0%
        140bp
        147bp
        27%
        19.7M
        135_M4_2
        48.4%
        49.0%
        150bp
        150bp
        27%
        19.7M
        5.6%
        59.6%
        49.0%
        140bp
        147bp
        18%
        19.7M
        139_M4
        0.1
        41.7%
        1.00
        15.7M
        53.4%
        0.60%
        2.0M
        31.4M
        100.0%
        100.0%
        0.2%
        31.4M
        33.4M
        33.4M
        100.0%
        21.6M
        15.7M
        72.7%
        15.0M
        69.6%
        0.7M
        139_M4_1
        53.2%
        48.0%
        150bp
        150bp
        45%
        21.6M
        5.0%
        64.9%
        49.0%
        141bp
        147bp
        27%
        21.6M
        139_M4_2
        48.6%
        49.0%
        150bp
        150bp
        27%
        21.6M
        5.1%
        60.1%
        49.0%
        140bp
        147bp
        18%
        21.6M
        140_M4
        0.1
        42.1%
        1.01
        22.4M
        53.3%
        0.60%
        2.6M
        44.7M
        100.0%
        100.0%
        0.2%
        44.7M
        47.4M
        47.4M
        100.0%
        29.0M
        22.4M
        77.2%
        21.5M
        74.4%
        0.8M
        140_M4_1
        53.6%
        48.0%
        150bp
        150bp
        45%
        29.0M
        5.5%
        65.8%
        48.0%
        140bp
        147bp
        27%
        29.0M
        140_M4_2
        48.6%
        49.0%
        150bp
        150bp
        27%
        29.0M
        5.5%
        61.2%
        49.0%
        140bp
        147bp
        27%
        29.0M
        142_F5
        0.1
        51.2%
        1.00
        25.2M
        43.7%
        0.63%
        4.3M
        50.4M
        100.0%
        100.0%
        0.2%
        50.4M
        54.7M
        54.7M
        100.0%
        31.3M
        25.2M
        80.7%
        23.8M
        76.1%
        1.4M
        142_F5_1
        61.5%
        48.0%
        150bp
        150bp
        45%
        31.3M
        5.1%
        71.8%
        48.0%
        140bp
        147bp
        27%
        31.3M
        142_F5_2
        56.1%
        49.0%
        150bp
        150bp
        45%
        31.3M
        5.2%
        66.9%
        49.0%
        140bp
        147bp
        27%
        31.3M
        144_M4
        0.1
        46.5%
        1.01
        18.4M
        48.9%
        0.56%
        2.4M
        36.7M
        100.0%
        100.0%
        0.1%
        36.7M
        39.2M
        39.2M
        100.0%
        23.3M
        18.4M
        78.7%
        17.5M
        75.2%
        0.8M
        144_M4_1
        57.1%
        48.0%
        150bp
        150bp
        45%
        23.4M
        5.8%
        67.5%
        48.0%
        139bp
        147bp
        27%
        23.3M
        144_M4_2
        53.0%
        48.0%
        150bp
        150bp
        36%
        23.4M
        5.9%
        62.8%
        48.0%
        139bp
        147bp
        18%
        23.3M
        146_M4
        0.1
        45.4%
        1.02
        16.8M
        50.0%
        0.59%
        2.2M
        33.6M
        100.0%
        100.0%
        0.2%
        33.6M
        35.8M
        35.8M
        100.0%
        20.7M
        16.8M
        80.9%
        16.0M
        77.4%
        0.7M
        146_M4_1
        56.8%
        49.0%
        150bp
        150bp
        45%
        20.8M
        4.0%
        67.1%
        49.0%
        142bp
        147bp
        27%
        20.7M
        146_M4_2
        51.9%
        49.0%
        150bp
        150bp
        27%
        20.8M
        4.1%
        62.3%
        49.0%
        142bp
        147bp
        9%
        20.7M
        152_M4
        0.1
        48.6%
        0.99
        16.8M
        45.9%
        0.59%
        3.0M
        33.7M
        100.0%
        100.0%
        0.2%
        33.7M
        36.7M
        36.7M
        100.0%
        20.9M
        16.8M
        80.5%
        15.8M
        75.7%
        1.0M
        152_M4_1
        58.1%
        49.0%
        150bp
        150bp
        36%
        20.9M
        5.5%
        66.7%
        49.0%
        140bp
        147bp
        18%
        20.9M
        152_M4_2
        56.8%
        49.0%
        150bp
        150bp
        36%
        20.9M
        5.6%
        66.5%
        49.0%
        140bp
        147bp
        18%
        20.9M
        154_F5
        0.1
        47.8%
        1.02
        19.0M
        47.3%
        0.59%
        2.9M
        37.9M
        100.0%
        100.0%
        0.3%
        37.9M
        40.8M
        40.8M
        100.0%
        24.3M
        19.0M
        78.1%
        18.1M
        74.4%
        0.9M
        154_F5_1
        57.1%
        48.0%
        150bp
        150bp
        45%
        24.3M
        3.7%
        68.4%
        48.0%
        142bp
        147bp
        27%
        24.3M
        154_F5_2
        54.6%
        49.0%
        150bp
        150bp
        36%
        24.3M
        3.7%
        65.4%
        49.0%
        142bp
        147bp
        27%
        24.3M
        158_F5
        0.1
        48.9%
        1.01
        15.4M
        46.1%
        0.58%
        2.4M
        30.9M
        100.0%
        100.0%
        0.2%
        30.9M
        33.2M
        33.2M
        100.0%
        20.0M
        15.4M
        77.0%
        14.6M
        72.9%
        0.8M
        158_F5_1
        59.5%
        48.0%
        150bp
        150bp
        45%
        20.1M
        5.4%
        68.8%
        48.0%
        140bp
        147bp
        27%
        20.0M
        158_F5_2
        55.1%
        48.0%
        150bp
        150bp
        36%
        20.1M
        5.5%
        64.2%
        48.0%
        140bp
        147bp
        18%
        20.0M
        159_F5
        0.1
        50.4%
        1.01
        18.4M
        44.6%
        0.60%
        3.0M
        36.8M
        100.0%
        100.0%
        0.2%
        36.8M
        39.7M
        39.7M
        100.0%
        23.6M
        18.4M
        77.8%
        17.4M
        73.6%
        1.0M
        159_F5_1
        61.8%
        48.0%
        150bp
        150bp
        45%
        23.7M
        4.5%
        70.9%
        48.0%
        141bp
        147bp
        27%
        23.6M
        159_F5_2
        56.6%
        49.0%
        150bp
        150bp
        36%
        23.7M
        4.6%
        66.3%
        49.0%
        141bp
        147bp
        27%
        23.6M
        173_F5
        0.1
        53.3%
        0.98
        18.6M
        41.1%
        0.57%
        3.9M
        37.2M
        100.0%
        100.0%
        0.3%
        37.2M
        41.2M
        41.2M
        100.0%
        23.4M
        18.6M
        79.4%
        17.5M
        74.5%
        1.2M
        173_F5_1
        62.3%
        49.0%
        150bp
        150bp
        45%
        23.5M
        6.3%
        71.9%
        48.0%
        139bp
        147bp
        27%
        23.4M
        173_F5_2
        60.5%
        49.0%
        150bp
        150bp
        36%
        23.5M
        6.4%
        69.4%
        49.0%
        139bp
        147bp
        18%
        23.4M
        176_F5
        0.1
        50.8%
        1.00
        19.8M
        43.7%
        0.56%
        3.8M
        39.6M
        100.0%
        100.0%
        0.3%
        39.6M
        43.4M
        43.4M
        100.0%
        24.8M
        19.8M
        80.0%
        18.6M
        75.2%
        1.2M
        176_F5_1
        59.8%
        48.0%
        150bp
        150bp
        45%
        24.8M
        5.1%
        70.0%
        48.0%
        140bp
        147bp
        27%
        24.8M
        176_F5_2
        57.7%
        49.0%
        150bp
        150bp
        36%
        24.8M
        5.2%
        67.8%
        49.0%
        140bp
        147bp
        18%
        24.8M
        35_F1
        0.1
        51.7%
        1.03
        19.7M
        42.7%
        0.59%
        3.8M
        39.3M
        100.0%
        100.0%
        0.3%
        39.3M
        43.2M
        43.2M
        100.0%
        25.5M
        19.7M
        77.0%
        18.5M
        72.4%
        1.2M
        35_F1_1
        61.0%
        49.0%
        150bp
        150bp
        45%
        25.6M
        5.2%
        71.0%
        49.0%
        140bp
        147bp
        27%
        25.5M
        35_F1_2
        56.5%
        49.0%
        150bp
        150bp
        36%
        25.6M
        5.3%
        66.7%
        49.0%
        140bp
        147bp
        18%
        25.5M
        36_F1
        0.1
        49.2%
        1.00
        18.9M
        45.4%
        0.55%
        3.4M
        37.9M
        100.0%
        100.0%
        0.4%
        37.9M
        41.3M
        41.3M
        100.0%
        25.2M
        18.9M
        75.3%
        17.9M
        71.2%
        1.0M
        36_F1_1
        60.0%
        49.0%
        150bp
        150bp
        45%
        25.2M
        5.0%
        70.2%
        49.0%
        141bp
        147bp
        27%
        25.2M
        36_F1_2
        55.2%
        49.0%
        150bp
        150bp
        36%
        25.2M
        5.0%
        66.0%
        49.0%
        141bp
        147bp
        18%
        25.2M
        45_M3
        0.1
        51.2%
        1.00
        17.6M
        43.8%
        0.57%
        3.0M
        35.3M
        100.0%
        100.0%
        0.3%
        35.3M
        38.3M
        38.3M
        100.0%
        21.8M
        17.7M
        81.1%
        16.7M
        76.7%
        1.0M
        45_M3_1
        62.1%
        48.0%
        150bp
        150bp
        45%
        21.8M
        4.3%
        71.2%
        48.0%
        142bp
        147bp
        27%
        21.8M
        45_M3_2
        57.3%
        49.0%
        150bp
        150bp
        36%
        21.8M
        4.4%
        66.8%
        49.0%
        141bp
        147bp
        18%
        21.8M
        51_F1
        0.1
        50.3%
        1.01
        19.4M
        43.5%
        0.57%
        4.3M
        38.7M
        100.0%
        100.0%
        0.5%
        38.7M
        43.0M
        43.0M
        100.0%
        24.5M
        19.4M
        78.9%
        18.1M
        74.0%
        1.2M
        51_F1_1
        60.2%
        49.0%
        150bp
        150bp
        45%
        24.6M
        5.5%
        70.2%
        49.0%
        140bp
        147bp
        27%
        24.5M
        51_F1_2
        57.0%
        49.0%
        150bp
        150bp
        36%
        24.6M
        5.6%
        66.6%
        49.0%
        140bp
        147bp
        18%
        24.5M
        54_F1
        0.1
        55.8%
        1.01
        18.5M
        36.9%
        0.56%
        6.0M
        37.0M
        100.0%
        100.0%
        0.9%
        37.0M
        42.9M
        42.9M
        100.0%
        23.6M
        18.5M
        78.3%
        16.9M
        71.8%
        1.5M
        54_F1_1
        64.3%
        49.0%
        150bp
        150bp
        45%
        23.6M
        4.3%
        73.7%
        49.0%
        142bp
        147bp
        36%
        23.6M
        54_F1_2
        62.0%
        50.0%
        150bp
        150bp
        45%
        23.6M
        4.5%
        71.3%
        50.0%
        141bp
        147bp
        36%
        23.6M
        55_F1
        0.1
        55.4%
        1.00
        17.9M
        36.9%
        0.55%
        6.2M
        35.8M
        100.0%
        100.0%
        0.9%
        35.8M
        42.0M
        42.0M
        100.0%
        22.9M
        17.9M
        78.1%
        16.3M
        70.9%
        1.6M
        55_F1_1
        64.4%
        48.0%
        150bp
        150bp
        45%
        22.9M
        4.7%
        73.3%
        48.0%
        141bp
        147bp
        27%
        22.9M
        55_F1_2
        60.7%
        49.0%
        150bp
        150bp
        36%
        22.9M
        4.7%
        69.7%
        48.0%
        141bp
        147bp
        18%
        22.9M
        65_F1
        0.1
        49.2%
        0.99
        20.4M
        44.2%
        0.58%
        4.8M
        40.8M
        100.0%
        100.0%
        0.5%
        40.8M
        45.6M
        45.6M
        100.0%
        25.9M
        20.4M
        78.6%
        19.1M
        73.5%
        1.3M
        65_F1_1
        59.8%
        48.0%
        150bp
        150bp
        36%
        26.0M
        6.5%
        70.2%
        48.0%
        139bp
        147bp
        18%
        25.9M
        65_F1_2
        55.0%
        48.0%
        150bp
        150bp
        36%
        26.0M
        6.5%
        65.7%
        48.0%
        139bp
        147bp
        18%
        25.9M
        66_F1
        0.1
        48.8%
        1.00
        22.3M
        45.6%
        0.60%
        4.1M
        44.5M
        100.0%
        100.0%
        0.3%
        44.5M
        48.6M
        48.6M
        100.0%
        28.3M
        22.3M
        78.5%
        21.0M
        74.0%
        1.3M
        66_F1_1
        59.8%
        48.0%
        150bp
        150bp
        36%
        28.4M
        4.9%
        70.3%
        48.0%
        141bp
        147bp
        18%
        28.3M
        66_F1_2
        55.5%
        49.0%
        150bp
        150bp
        36%
        28.4M
        5.0%
        65.5%
        48.0%
        141bp
        147bp
        18%
        28.3M
        69_M3
        0.1
        50.5%
        1.00
        18.3M
        44.2%
        0.56%
        3.3M
        36.7M
        100.0%
        100.0%
        0.2%
        36.7M
        40.0M
        40.0M
        100.0%
        22.6M
        18.4M
        81.2%
        17.3M
        76.4%
        1.1M
        69_M3_1
        60.7%
        48.0%
        150bp
        150bp
        36%
        22.6M
        4.7%
        69.7%
        48.0%
        141bp
        147bp
        18%
        22.6M
        69_M3_2
        56.3%
        49.0%
        150bp
        150bp
        36%
        22.6M
        4.7%
        65.7%
        48.0%
        141bp
        147bp
        18%
        22.6M
        70_M3
        0.1
        51.4%
        1.00
        18.0M
        43.8%
        0.55%
        2.9M
        36.0M
        100.0%
        100.0%
        0.3%
        36.0M
        38.9M
        38.9M
        100.0%
        22.5M
        18.0M
        80.2%
        17.1M
        75.9%
        1.0M
        70_M3_1
        60.6%
        48.0%
        150bp
        150bp
        36%
        22.5M
        6.2%
        70.1%
        49.0%
        139bp
        147bp
        27%
        22.5M
        70_M3_2
        56.9%
        49.0%
        150bp
        150bp
        27%
        22.5M
        6.3%
        66.9%
        49.0%
        139bp
        147bp
        9%
        22.5M
        71_M3
        0.1
        48.5%
        1.01
        14.3M
        43.6%
        0.52%
        4.3M
        28.6M
        100.0%
        100.0%
        0.8%
        28.6M
        32.9M
        32.9M
        100.0%
        18.0M
        14.3M
        79.5%
        13.2M
        73.5%
        1.1M
        71_M3_1
        58.4%
        47.0%
        150bp
        150bp
        36%
        18.0M
        5.8%
        67.6%
        47.0%
        139bp
        147bp
        18%
        18.0M
        71_M3_2
        54.9%
        47.0%
        150bp
        150bp
        36%
        18.0M
        5.9%
        63.8%
        47.0%
        139bp
        147bp
        18%
        18.0M
        73_M3
        0.1
        48.5%
        1.01
        15.5M
        46.8%
        0.57%
        2.3M
        30.9M
        100.0%
        100.0%
        0.2%
        30.9M
        33.2M
        33.2M
        100.0%
        19.4M
        15.5M
        79.5%
        14.7M
        75.5%
        0.8M
        73_M3_1
        58.8%
        48.0%
        150bp
        150bp
        36%
        19.5M
        5.9%
        68.5%
        48.0%
        139bp
        147bp
        18%
        19.4M
        73_M3_2
        54.9%
        48.0%
        150bp
        150bp
        36%
        19.5M
        6.0%
        64.9%
        48.0%
        139bp
        147bp
        18%
        19.4M
        74_M3
        0.1
        50.4%
        1.01
        18.7M
        43.8%
        0.57%
        3.8M
        37.3M
        100.0%
        100.0%
        0.4%
        37.3M
        41.1M
        41.1M
        100.0%
        23.8M
        18.7M
        78.5%
        17.5M
        73.7%
        1.1M
        74_M3_1
        60.8%
        48.0%
        150bp
        150bp
        36%
        23.8M
        6.6%
        70.3%
        48.0%
        138bp
        147bp
        18%
        23.8M
        74_M3_2
        56.3%
        48.0%
        150bp
        150bp
        36%
        23.8M
        6.6%
        66.4%
        48.0%
        138bp
        147bp
        18%
        23.8M
        79_M3
        0.1
        50.4%
        1.00
        19.4M
        44.4%
        0.54%
        3.4M
        38.9M
        100.0%
        100.0%
        0.3%
        38.9M
        42.3M
        42.3M
        100.0%
        23.6M
        19.5M
        82.4%
        18.3M
        77.7%
        1.1M
        79_M3_1
        59.5%
        49.0%
        150bp
        150bp
        36%
        23.6M
        4.1%
        69.6%
        49.0%
        142bp
        147bp
        18%
        23.6M
        79_M3_2
        57.8%
        49.0%
        150bp
        150bp
        27%
        23.6M
        4.2%
        67.5%
        49.0%
        142bp
        147bp
        9%
        23.6M
        83_F3
        0.1
        49.4%
        0.99
        21.2M
        45.2%
        0.59%
        3.8M
        42.5M
        100.0%
        100.0%
        0.3%
        42.5M
        46.3M
        46.3M
        100.0%
        26.6M
        21.2M
        80.0%
        20.0M
        75.4%
        1.2M
        83_F3_1
        59.1%
        48.0%
        150bp
        150bp
        36%
        26.6M
        5.4%
        69.7%
        48.0%
        140bp
        147bp
        18%
        26.6M
        83_F3_2
        55.7%
        49.0%
        150bp
        150bp
        36%
        26.6M
        5.5%
        66.0%
        49.0%
        140bp
        147bp
        18%
        26.6M

        Sample status checks

        Reports on sample strandedness status, and any failures in trimming or mapping.

        Strandedness Checks

        Showing 0/35 rows and 7/7 columns.
        SampleStatusStrand inference methodProvided strandednessInferred strandednessSense (%)Antisense (%)Unstranded (%)
        101_F3
        pass
        RSeQC
        reverse
        reverse
        3.1
        96.0
        1.0
        114_F4
        pass
        RSeQC
        reverse
        reverse
        2.3
        97.2
        0.5
        117_F3
        pass
        RSeQC
        reverse
        reverse
        2.0
        97.6
        0.4
        119_F3
        pass
        RSeQC
        reverse
        reverse
        1.8
        97.6
        0.6
        123_F4
        pass
        RSeQC
        reverse
        reverse
        2.8
        96.4
        0.9
        124_F4
        pass
        RSeQC
        reverse
        reverse
        3.1
        95.9
        1.0
        131_M4
        pass
        RSeQC
        reverse
        reverse
        3.2
        95.9
        0.9
        134_F5
        pass
        RSeQC
        reverse
        reverse
        3.2
        95.9
        0.9
        135_M4
        pass
        RSeQC
        reverse
        reverse
        2.6
        96.6
        0.8
        139_M4
        pass
        RSeQC
        reverse
        reverse
        2.5
        96.8
        0.7
        140_M4
        pass
        RSeQC
        reverse
        reverse
        3.4
        95.5
        1.1
        142_F5
        pass
        RSeQC
        reverse
        reverse
        2.9
        96.4
        0.7
        144_M4
        pass
        RSeQC
        reverse
        reverse
        2.7
        96.4
        0.9
        146_M4
        pass
        RSeQC
        reverse
        reverse
        2.4
        97.0
        0.6
        152_M4
        pass
        RSeQC
        reverse
        reverse
        2.1
        97.2
        0.7
        154_F5
        pass
        RSeQC
        reverse
        reverse
        2.6
        96.7
        0.7
        158_F5
        pass
        RSeQC
        reverse
        reverse
        2.1
        97.1
        0.8
        159_F5
        pass
        RSeQC
        reverse
        reverse
        2.3
        97.2
        0.6
        173_F5
        pass
        RSeQC
        reverse
        reverse
        2.1
        97.3
        0.6
        176_F5
        pass
        RSeQC
        reverse
        reverse
        2.2
        97.2
        0.6
        35_F1
        pass
        RSeQC
        reverse
        reverse
        2.3
        97.1
        0.6
        36_F1
        pass
        RSeQC
        reverse
        reverse
        2.5
        96.8
        0.7
        45_M3
        pass
        RSeQC
        reverse
        reverse
        2.2
        97.4
        0.4
        51_F1
        pass
        RSeQC
        reverse
        reverse
        2.6
        96.6
        0.8
        54_F1
        pass
        RSeQC
        reverse
        reverse
        1.8
        97.8
        0.3
        55_F1
        pass
        RSeQC
        reverse
        reverse
        1.8
        97.8
        0.4
        65_F1
        pass
        RSeQC
        reverse
        reverse
        2.5
        96.7
        0.8
        66_F1
        pass
        RSeQC
        reverse
        reverse
        2.7
        96.5
        0.9
        69_M3
        pass
        RSeQC
        reverse
        reverse
        2.5
        96.9
        0.7
        70_M3
        pass
        RSeQC
        reverse
        reverse
        2.5
        96.8
        0.6
        71_M3
        pass
        RSeQC
        reverse
        reverse
        2.1
        97.3
        0.6
        73_M3
        pass
        RSeQC
        reverse
        reverse
        2.0
        97.5
        0.5
        74_M3
        pass
        RSeQC
        reverse
        reverse
        2.4
        96.9
        0.7
        79_M3
        pass
        RSeQC
        reverse
        reverse
        2.6
        96.8
        0.6
        83_F3
        pass
        RSeQC
        reverse
        reverse
        2.8
        96.6
        0.7

        FastQC (raw)

        This section of the report shows FastQC results before adapter trimming.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Created with MultiQC

        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Created with MultiQC

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Created with MultiQC

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Created with MultiQC

        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Created with MultiQC

        Sequence Length Distribution

        All samples have sequences of a single length (150bp)

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        Created with MultiQC

        Overrepresented sequences by sample

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        Created with MultiQC

        Top overrepresented sequences

        Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

        Showing 0/20 rows and 3/3 columns.
        Overrepresented sequenceReportsOccurrences% of all reads
        CGTTGGATTTCACTTTTGGAGCTGGGGATGGGTAAGGCTTTGGTGATGCA
        13
        354289
        0.0214%
        TCACAAATCTCCATCCAACGGCCGAACGACAGGAAAACACCAGCAATAAG
        11
        336476
        0.0203%
        ACACAAATCTCCATCCAACGGCCGAACGACAGGAAAACACCAGCAATAAG
        11
        349336
        0.0211%
        CGTCATATCTATAAGTGGACGGCTATCAACTTCAGGAATTAATTTAAAGC
        11
        303043
        0.0183%
        AGGGAAGTGTATGAGCCGTGCTTTGTCTTCCAATGGCCCATTAGCAAGAC
        9
        265838
        0.0160%
        AGTTGGAATAATCAAAACACTGCAAGAGCCAGCGAGGAATACAAACTCCA
        9
        255860
        0.0154%
        CGGGAAATATAAAATGCAGTATGCTGAAGAGAGGGCACAGCATTACACCT
        6
        154411
        0.0093%
        ACTCTGTTTGACTCCCGTCTCCGGTGTTTCATTTCAATAATGCAGAAGTG
        6
        167961
        0.0101%
        TCTCAGATCAGATCCAGTCACCAGAACTTGAACCTGAAGCTGAACCAGAA
        5
        241229
        0.0146%
        GGTTGGATTTCACTTTTGGAGCTGGGGATGGGTAAGGCTTTGGTGATGCA
        5
        124404
        0.0075%
        AGTTGAACTCTGCTGACAGCGATGGAGAACAGACGCTGGCCATCTGTGAT
        4
        159886
        0.0096%
        ACTCAGATCAGATCCAGTCACCAGAACTTGAACCTGAAGCTGAACCAGAA
        4
        201984
        0.0122%
        TGTTGAACTCTGCTGACAGCGATGGAGAACAGACGCTGGCCATCTGTGAT
        3
        124555
        0.0075%
        AGTCGAAACCAAAAACTCCACATTTTTCATTTTAACAGCTATTTGTTCCC
        3
        73455
        0.0044%
        AGCAGGATATGTAGATTTTTTTGACAGCTCGACACACAAATAATCTTTTG
        3
        67835
        0.0041%
        TGGGAAGTGTATGAGCCGTGCTTTGTCTTCCAATGGCCCATTAGCAAGAC
        3
        84064
        0.0051%
        ATGATGATGTAATCAGAGTTACAGAAATCAGGGAAGATTTTGTTGAGCTT
        2
        53048
        0.0032%
        AGTGAGACTCCCTTTTTTAAATGGAGCTGGTTTCACTTCTGCATTATTGA
        2
        49833
        0.0030%
        AGCCTCTTCTACTTACACATGAGAGTACAGAAAGTTTGCATTAAGCATTA
        2
        48943
        0.0030%
        GCTCAGATCAGATCCAGTCACCAGAACTTGAACCTGAAGCTGAACCAGAA
        2
        86283
        0.0052%

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        Created with MultiQC

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        Created with MultiQC

        Cutadapt

        Finds and removes adapter sequences, primers, poly-A tails, and other types of unwanted sequences.URL: https://cutadapt.readthedocs.ioDOI: 10.14806/ej.17.1.200

        Filtered Reads

        This plot shows the number of reads (SE) / pairs (PE) removed by Cutadapt.

        Created with MultiQC

        Trimmed Sequence Lengths (3')

        This plot shows the number of reads with certain lengths of adapter trimmed for the 3' end.

        Obs/Exp shows the raw counts divided by the number expected due to sequencing errors. A defined peak may be related to adapter length.

        See the cutadapt documentation for more information on how these numbers are generated.

        Created with MultiQC

        FastQC (trimmed)

        This section of the report shows FastQC results after adapter trimming.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Created with MultiQC

        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Created with MultiQC

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Created with MultiQC

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Created with MultiQC

        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Created with MultiQC

        Sequence Length Distribution

        The distribution of fragment sizes (read lengths) found. See the FastQC help

        Created with MultiQC

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        Created with MultiQC

        Overrepresented sequences by sample

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        Created with MultiQC

        Top overrepresented sequences

        Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

        Showing 0/20 rows and 3/3 columns.
        Overrepresented sequenceReportsOccurrences% of all reads
        CACAAATCTCCATCCAACGGCCGAACGACAGGAAAACACCAGCAATAAGA
        35
        2266084
        0.1369%
        GTTGGATTTCACTTTTGGAGCTGGGGATGGGTAAGGCTTTGGTGATGCAG
        28
        1485567
        0.0897%
        GTCATATCTATAAGTGGACGGCTATCAACTTCAGGAATTAATTTAAAGCT
        28
        1614682
        0.0975%
        GGGAAATATAAAATGCAGTATGCTGAAGAGAGGGCACAGCATTACACCTA
        28
        1411292
        0.0852%
        GGGAAGTGTATGAGCCGTGCTTTGTCTTCCAATGGCCCATTAGCAAGACT
        27
        1236885
        0.0747%
        CTCAGATCAGATCCAGTCACCAGAACTTGAACCTGAAGCTGAACCAGAAC
        26
        1444435
        0.0873%
        GTTGGAATAATCAAAACACTGCAAGAGCCAGCGAGGAATACAAACTCCAG
        26
        1219295
        0.0737%
        GTGAGACTCCCTTTTTTAAATGGAGCTGGTTTCACTTCTGCATTATTGAA
        25
        847708
        0.0512%
        CTCTGTTTGACTCCCGTCTCCGGTGTTTCATTTCAATAATGCAGAAGTGA
        25
        1026712
        0.0620%
        GTCGAAACCAAAAACTCCACATTTTTCATTTTAACAGCTATTTGTTCCCT
        24
        785907
        0.0475%
        GTTCATCCCATTCCCACTGCAGCTTCAGAGGAACCAAGCTGGCTATCCCT
        23
        724424
        0.0438%
        AAACGACACAAATCTCCATCCAACGGCCGAACGACAGGAAAACACCAGCA
        23
        689156
        0.0416%
        GCAGGATATGTAGATTTTTTTGACAGCTCGACACACAAATAATCTTTTGG
        21
        707280
        0.0427%
        GGAATAATCAAAACACTGCAAGAGCCAGCGAGGAATACAAACTCCAGTTA
        21
        727159
        0.0439%
        CTCAACTCTGAGAGCAGCGTGATGGAGTGTATCCAGCTTTGTCACTCCGA
        20
        580731
        0.0351%
        GTACTATTAAAATTGAAATTGTGTATCCCCTGATCATTTAAAGTGAATAA
        20
        653008
        0.0394%
        TGAGAACGTCTCCTCCCTCTCCATCTTCCTCCTCTCACTTCTGCTGCTGT
        19
        589344
        0.0356%
        GTCTTGTTGAGCAGCATGTCAGGAACTCCATCGTAGCGGTCCAGTGTGGT
        19
        591667
        0.0357%
        AAAAAATCTAAACCTGGGTCGAAACCAAAAACTCCACATTTTTCATTTTA
        18
        575206
        0.0347%
        GTCAGATATCTGTTGGATTTCACTTTTGGAGCTGGGGATGGGTAAGGCTT
        18
        560268
        0.0338%

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        Created with MultiQC

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        Created with MultiQC

        DupRadar

        DupRadar provides duplication rate quality control for RNA-Seq datasets. Highly expressed genes can be expected to have a lot of duplicate reads, but high numbers of duplicates at low read counts can indicate low library complexity with technical duplication. This plot shows the general linear models - a summary of the gene duplication distributions.URL: bioconductor.org/packages/release/bioc/html/dupRadar.html

        Created with MultiQC

        Picard

        Tools for manipulating high-throughput sequencing data.URL: http://broadinstitute.github.io/picard

        Mark Duplicates

        Number of reads, categorised by duplication state. Pair counts are doubled - see help text for details.

        The table in the Picard metrics file contains some columns referring read pairs and some referring to single reads.

        To make the numbers in this plot sum correctly, values referring to pairs are doubled according to the scheme below:

        • READS_IN_DUPLICATE_PAIRS = 2 * READ_PAIR_DUPLICATES
        • READS_IN_UNIQUE_PAIRS = 2 * (READ_PAIRS_EXAMINED - READ_PAIR_DUPLICATES)
        • READS_IN_UNIQUE_UNPAIRED = UNPAIRED_READS_EXAMINED - UNPAIRED_READ_DUPLICATES
        • READS_IN_DUPLICATE_PAIRS_OPTICAL = 2 * READ_PAIR_OPTICAL_DUPLICATES
        • READS_IN_DUPLICATE_PAIRS_NONOPTICAL = READS_IN_DUPLICATE_PAIRS - READS_IN_DUPLICATE_PAIRS_OPTICAL
        • READS_IN_DUPLICATE_UNPAIRED = UNPAIRED_READ_DUPLICATES
        • READS_UNMAPPED = UNMAPPED_READS
        Created with MultiQC

        QualiMap

        Quality control of alignment data and its derivatives like feature counts.URL: http://qualimap.bioinfo.cipf.esDOI: 10.1093/bioinformatics/btv566; 10.1093/bioinformatics/bts503

        Genomic origin of reads

        Classification of mapped reads as originating in exonic, intronic or intergenic regions. These can be displayed as either the number or percentage of mapped reads.

        There are currently three main approaches to map reads to transcripts in an RNA-seq experiment: mapping reads to a reference genome to identify expressed transcripts that are annotated (and discover those that are unknown), mapping reads to a reference transcriptome, and de novo assembly of transcript sequences (Conesa et al. 2016).

        For RNA-seq QC analysis, QualiMap can be used to assess alignments produced by the first of these approaches. For input, it requires a GTF annotation file along with a reference genome, which can be used to reconstruct the exon structure of known transcripts. This allows mapped reads to be grouped by whether they originate in an exonic region (for QualiMap, this may include 5′ and 3′ UTR regions as well as protein-coding exons), an intron, or an intergenic region (see the Qualimap 2 documentation).

        The inferred genomic origins of RNA-seq reads are presented here as a bar graph showing either the number or percentage of mapped reads in each read dataset that have been assigned to each type of genomic region. This graph can be used to assess the proportion of useful reads in an RNA-seq experiment. That proportion can be reduced by the presence of intron sequences, especially if depletion of ribosomal RNA was used during sample preparation (Sims et al. 2014). It can also be reduced by off-target transcripts, which are detected in greater numbers at the sequencing depths needed to detect poorly-expressed transcripts (Tarazona et al. 2011).

        Created with MultiQC

        Gene Coverage Profile

        Mean distribution of coverage depth across the length of all mapped transcripts.

        There are currently three main approaches to map reads to transcripts in an RNA-seq experiment: mapping reads to a reference genome to identify expressed transcripts that are annotated (and discover those that are unknown), mapping reads to a reference transcriptome, and de novo assembly of transcript sequences (Conesa et al. 2016).

        For RNA-seq QC analysis, QualiMap can be used to assess alignments produced by the first of these approaches. For input, it requires a GTF annotation file along with a reference genome, which can be used to reconstruct the exon structure of known transcripts. QualiMap uses this information to calculate the depth of coverage along the length of each annotated transcript. For a set of reads mapped to a transcript, the depth of coverage at a given base position is the number of high-quality reads that map to the transcript at that position (Sims et al. 2014).

        QualiMap calculates coverage depth at every base position of each annotated transcript. To enable meaningful comparison between transcripts, base positions are rescaled to relative positions expressed as percentage distance along each transcript (0%, 1%, …, 99%). For the set of transcripts with at least one mapped read, QualiMap plots the cumulative mapped-read depth (y-axis) at each relative transcript position (x-axis). This plot shows the gene coverage profile across all mapped transcripts for each read dataset. It provides a visual way to assess positional biases, such as an accumulation of mapped reads at the 3′ end of transcripts, which may indicate poor RNA quality in the original sample (Conesa et al. 2016).

        The Normalised plot is calculated by MultiQC to enable comparison of samples with varying sequencing depth. The cumulative mapped-read depth at each position across the averaged transcript position are divided by the total for that sample across the entire averaged transcript.

        Created with MultiQC

        RSeQC

        Evaluates high throughput RNA-seq data.URL: http://rseqc.sourceforge.netDOI: 10.1093/bioinformatics/bts356

        Read Distribution

        Read Distribution calculates how mapped reads are distributed over genome features.

        Created with MultiQC

        Inner Distance

        Inner Distance calculates the inner distance (or insert size) between two paired RNA reads. Note that this can be negative if fragments overlap.

        Created with MultiQC

        Read Duplication

        read_duplication.py calculates how many alignment positions have a certain number of exact duplicates. Note - plot truncated at 500 occurrences and binned.

        Created with MultiQC

        Junction Annotation

        Junction annotation compares detected splice junctions to a reference gene model. An RNA read can be spliced 2 or more times, each time is called a splicing event.

        Created with MultiQC

        Junction Saturation

        Junction Saturation counts the number of known splicing junctions that are observed in each dataset. If sequencing depth is sufficient, all (annotated) splice junctions should be rediscovered, resulting in a curve that reaches a plateau. Missing low abundance splice junctions can affect downstream analysis.

        Click a line to see the data side by side (as in the original RSeQC plot).

        Created with MultiQC

        Infer experiment

        Infer experiment counts the percentage of reads and read pairs that match the strandedness of overlapping transcripts. It can be used to infer whether RNA-seq library preps are stranded (sense or antisense).

        Created with MultiQC

        Bam Stat

        All numbers reported in millions.

        Created with MultiQC

        Samtools

        Toolkit for interacting with BAM/CRAM files.URL: http://www.htslib.orgDOI: 10.1093/bioinformatics/btp352

        Percent mapped

        Alignment metrics from samtools stats; mapped vs. unmapped reads vs. reads mapped with MQ0.

        For a set of samples that have come from the same multiplexed library, similar numbers of reads for each sample are expected. Large differences in numbers might indicate issues during the library preparation process. Whilst large differences in read numbers may be controlled for in downstream processings (e.g. read count normalisation), you may wish to consider whether the read depths achieved have fallen below recommended levels depending on the applications.

        Low alignment rates could indicate contamination of samples (e.g. adapter sequences), low sequencing quality or other artefacts. These can be further investigated in the sequence level QC (e.g. from FastQC).

        Reads mapped with MQ0 often indicate that the reads are ambiguously mapped to multiple locations in the reference sequence. This can be due to repetitive regions in the genome, the presence of alternative contigs in the reference, or due to reads that are too short to be uniquely mapped. These reads are often filtered out in downstream analyses.

        Created with MultiQC

        Alignment stats

        This module parses the output from samtools stats. All numbers in millions.

        Created with MultiQC

        Flagstat

        This module parses the output from samtools flagstat

        Created with MultiQC

        Mapped reads per contig

        The samtools idxstats tool counts the number of mapped reads per chromosome / contig. Chromosomes with < 0.1% of the total aligned reads are omitted from this plot.

        Created with MultiQC

        STAR

        Universal RNA-seq aligner.URL: https://github.com/alexdobin/STARDOI: 10.1093/bioinformatics/bts635

        Summary Statistics

        Summary statistics from the STAR alignment

        Showing 0/35 rows and 10/19 columns.
        Sample NameTotal readsAlignedAlignedUniq alignedUniq alignedMultimappedAvg. read lenAvg. mapped lenSplicesAnnotated splicesGT/AG splicesGC/AG splicesAT/AC splicesNon-canonical splicesMismatch rateDel rateDel lenIns rateIns len
        101_F3
        29.6M
        22.6M
        76.2%
        21.3M
        72.0%
        1.2M
        282.0bp
        279.9bp
        20.7M
        20.7M
        20.5M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        2.0bp
        0.0%
        1.6bp
        114_F4
        23.0M
        17.2M
        74.8%
        16.1M
        70.1%
        1.1M
        282.0bp
        280.2bp
        15.5M
        15.5M
        15.4M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        2.0bp
        0.0%
        1.4bp
        117_F3
        20.9M
        15.9M
        76.0%
        14.7M
        70.5%
        1.2M
        280.0bp
        278.4bp
        14.1M
        14.1M
        14.0M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        1.9bp
        0.0%
        1.8bp
        119_F3
        15.9M
        12.3M
        77.2%
        11.8M
        74.2%
        0.5M
        280.0bp
        278.7bp
        11.0M
        11.0M
        11.0M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        1.9bp
        0.0%
        1.7bp
        123_F4
        20.7M
        16.3M
        78.9%
        15.6M
        75.6%
        0.7M
        280.0bp
        278.9bp
        14.8M
        14.8M
        14.7M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        2.0bp
        0.0%
        1.6bp
        124_F4
        23.2M
        19.4M
        83.4%
        18.8M
        80.8%
        0.6M
        280.0bp
        280.2bp
        17.6M
        17.6M
        17.5M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        2.1bp
        0.0%
        1.6bp
        131_M4
        28.9M
        23.2M
        80.5%
        22.1M
        76.7%
        1.1M
        284.0bp
        282.5bp
        22.3M
        22.3M
        22.1M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        2.0bp
        0.0%
        1.4bp
        134_F5
        28.7M
        22.1M
        77.1%
        20.9M
        72.9%
        1.2M
        284.0bp
        281.3bp
        20.7M
        20.7M
        20.6M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        2.0bp
        0.0%
        1.6bp
        135_M4
        19.7M
        15.5M
        78.8%
        14.7M
        74.9%
        0.8M
        281.0bp
        279.6bp
        13.4M
        13.4M
        13.3M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        2.0bp
        0.0%
        1.6bp
        139_M4
        21.6M
        15.7M
        72.7%
        15.0M
        69.6%
        0.7M
        283.0bp
        279.8bp
        14.6M
        14.6M
        14.5M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        2.0bp
        0.0%
        1.6bp
        140_M4
        29.0M
        22.4M
        77.2%
        21.5M
        74.4%
        0.8M
        281.0bp
        279.5bp
        20.7M
        20.6M
        20.5M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        2.1bp
        0.0%
        1.6bp
        142_F5
        31.3M
        25.2M
        80.7%
        23.8M
        76.1%
        1.4M
        282.0bp
        280.8bp
        23.6M
        23.5M
        23.4M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        2.0bp
        0.0%
        1.9bp
        144_M4
        23.3M
        18.4M
        78.7%
        17.5M
        75.2%
        0.8M
        280.0bp
        279.1bp
        16.8M
        16.8M
        16.7M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        2.1bp
        0.0%
        1.7bp
        146_M4
        20.7M
        16.8M
        80.9%
        16.0M
        77.4%
        0.7M
        285.0bp
        283.7bp
        16.2M
        16.2M
        16.1M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        2.1bp
        0.0%
        1.5bp
        152_M4
        20.9M
        16.8M
        80.5%
        15.8M
        75.7%
        1.0M
        281.0bp
        279.7bp
        15.3M
        15.2M
        15.1M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        1.9bp
        0.0%
        1.5bp
        154_F5
        24.3M
        19.0M
        78.1%
        18.1M
        74.4%
        0.9M
        287.0bp
        284.6bp
        17.9M
        17.8M
        17.8M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        2.0bp
        0.0%
        2.0bp
        158_F5
        20.0M
        15.4M
        77.0%
        14.6M
        72.9%
        0.8M
        282.0bp
        279.9bp
        14.0M
        14.0M
        13.9M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        2.0bp
        0.0%
        1.7bp
        159_F5
        23.6M
        18.4M
        77.8%
        17.4M
        73.6%
        1.0M
        284.0bp
        282.3bp
        16.9M
        16.8M
        16.8M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        1.9bp
        0.0%
        1.7bp
        173_F5
        23.4M
        18.6M
        79.4%
        17.5M
        74.5%
        1.2M
        279.0bp
        277.6bp
        16.8M
        16.8M
        16.7M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        1.8bp
        0.0%
        1.9bp
        176_F5
        24.8M
        19.8M
        80.0%
        18.6M
        75.2%
        1.2M
        282.0bp
        281.1bp
        18.2M
        18.2M
        18.1M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        2.0bp
        0.0%
        1.7bp
        35_F1
        25.5M
        19.7M
        77.0%
        18.5M
        72.4%
        1.2M
        282.0bp
        279.9bp
        18.8M
        18.8M
        18.7M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        2.0bp
        0.0%
        1.6bp
        36_F1
        25.2M
        18.9M
        75.3%
        17.9M
        71.2%
        1.0M
        283.0bp
        280.3bp
        18.4M
        18.3M
        18.3M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        2.0bp
        0.0%
        1.3bp
        45_M3
        21.8M
        17.7M
        81.1%
        16.7M
        76.7%
        1.0M
        285.0bp
        283.1bp
        16.8M
        16.7M
        16.7M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        2.0bp
        0.1%
        1.8bp
        51_F1
        24.5M
        19.4M
        78.9%
        18.1M
        74.0%
        1.2M
        281.0bp
        279.2bp
        18.2M
        18.2M
        18.1M
        0.1M
        0.0M
        0.1M
        0.5%
        0.0%
        1.9bp
        0.0%
        1.6bp
        54_F1
        23.6M
        18.5M
        78.3%
        16.9M
        71.8%
        1.5M
        285.0bp
        282.2bp
        19.6M
        19.6M
        19.5M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        2.1bp
        0.0%
        1.4bp
        55_F1
        22.9M
        17.9M
        78.1%
        16.3M
        70.9%
        1.6M
        284.0bp
        281.7bp
        16.3M
        16.2M
        16.2M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        2.0bp
        0.0%
        1.4bp
        65_F1
        25.9M
        20.4M
        78.6%
        19.1M
        73.5%
        1.3M
        279.0bp
        277.7bp
        18.7M
        18.6M
        18.5M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        1.9bp
        0.0%
        1.8bp
        66_F1
        28.3M
        22.3M
        78.5%
        21.0M
        74.0%
        1.3M
        283.0bp
        281.5bp
        20.2M
        20.2M
        20.1M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        1.9bp
        0.1%
        2.2bp
        69_M3
        22.6M
        18.4M
        81.2%
        17.3M
        76.4%
        1.1M
        284.0bp
        282.5bp
        16.7M
        16.6M
        16.6M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        2.0bp
        0.0%
        1.6bp
        70_M3
        22.5M
        18.0M
        80.2%
        17.1M
        75.9%
        1.0M
        279.0bp
        277.8bp
        16.4M
        16.3M
        16.3M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        1.9bp
        0.0%
        1.5bp
        71_M3
        18.0M
        14.3M
        79.5%
        13.2M
        73.5%
        1.1M
        280.0bp
        279.2bp
        12.7M
        12.7M
        12.7M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        2.1bp
        0.0%
        2.0bp
        73_M3
        19.4M
        15.5M
        79.5%
        14.7M
        75.5%
        0.8M
        280.0bp
        278.7bp
        14.2M
        14.2M
        14.1M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        1.9bp
        0.1%
        2.0bp
        74_M3
        23.8M
        18.7M
        78.5%
        17.5M
        73.7%
        1.1M
        278.0bp
        276.9bp
        16.3M
        16.2M
        16.2M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        1.9bp
        0.1%
        2.2bp
        79_M3
        23.6M
        19.5M
        82.4%
        18.3M
        77.7%
        1.1M
        285.0bp
        284.1bp
        18.1M
        18.1M
        18.0M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        1.9bp
        0.0%
        1.4bp
        83_F3
        26.6M
        21.2M
        80.0%
        20.0M
        75.4%
        1.2M
        281.0bp
        279.9bp
        19.5M
        19.5M
        19.4M
        0.1M
        0.0M
        0.0M
        0.5%
        0.0%
        2.0bp
        0.0%
        1.9bp

        Alignment Scores

        Created with MultiQC

        Sample relationships

        Plots interrogating sample relationships, based on final count matrices.

        STAR_SALMON DESeq2 sample similarity

        Created with MultiQC

        STAR_SALMON DESeq2 PCA plot

        Created with MultiQC

        Software Versions

        Software Versions lists versions of software tools extracted from file contents.

        GroupSoftwareVersion
        BEDTOOLS_GENOMECOV_FWbedtools2.31.1
        CUSTOM_GETCHROMSIZESgetchromsizes1.21
        CUSTOM_TX2GENEpython3.10.4
        DESEQ2_QC_STAR_SALMONbioconductor-deseq21.28.0
        r-base4.0.3
        DupRadarbioconductor-dupradar1.32.0
        FASTQCfastqc0.12.1
        GFFREADgffread0.12.7
        GTF2BEDperl5.26.2
        GTF_FILTERpython3.9.5
        MAKE_TRANSCRIPTS_FASTArsem1.3.1
        star2.7.10a
        PICARD_MARKDUPLICATESpicard3.1.1
        QUALIMAP_RNASEQqualimap2.3
        RSEQC_BAMSTATrseqc5.0.2
        RSEQC_INFEREXPERIMENTrseqc5.0.2
        RSEQC_INNERDISTANCErseqc5.0.2
        RSEQC_JUNCTIONANNOTATIONrseqc5.0.2
        RSEQC_JUNCTIONSATURATIONrseqc5.0.2
        RSEQC_READDISTRIBUTIONrseqc5.0.2
        RSEQC_READDUPLICATIONrseqc5.0.2
        SALMON_QUANTsalmon1.10.3
        SAMTOOLS_FLAGSTATsamtools1.21
        SAMTOOLS_IDXSTATSsamtools1.21
        SAMTOOLS_INDEXsamtools1.21
        SAMTOOLS_SORTsamtools1.21
        SAMTOOLS_STATSsamtools1.21
        SE_GENEbioconductor-summarizedexperiment1.32.0
        STAR_ALIGNgawk5.1.0
        samtools1.2
        star2.7.11b
        STAR_GENOMEGENERATEgawk5.1.0
        samtools1.2
        star2.7.11b
        STRINGTIE_STRINGTIEstringtie2.2.3
        TRIMGALOREcutadapt4.9
        trimgalore0.6.10
        TXIMETA_TXIMPORTbioconductor-tximeta1.20.1
        UCSC_BEDCLIPucsc377
        UCSC_BEDGRAPHTOBIGWIGucsc469
        WorkflowNextflow24.04.4
        nf-core/rnaseqv3.17.0-g00f924c

        nf-core/rnaseq Methods Description

        Suggested text and references to use when describing pipeline usage within the methods section of a publication.URL: https://github.com/nf-core/rnaseq

        Methods

        Data was processed using nf-core/rnaseq v3.17.0 (doi: 10.5281/zenodo.1400710) of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

        The pipeline was executed with Nextflow v24.04.4 (Di Tommaso et al., 2017) with the following command:

        nextflow run nf-core/rnaseq --input samplesheets/pit_samplesheet.csv --outdir output --fasta ChrAur2_renamed_fixed.fasta --gff ChrAurV1_fixed.gff '--extra_trimgalore_args=--clip_r1=1 --clip_r2=1' --skip_biotype_qc -profile singularity

        References

        • Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
        • Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
        • Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
        • da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192
        Notes:
        • The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
        • You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.

        nf-core/rnaseq Workflow Summary

        - this information is collected when the pipeline is started.URL: https://github.com/nf-core/rnaseq

        Input/output options

        input
        samplesheets/pit_samplesheet.csv
        outdir
        output

        Reference genome options

        fasta
        ChrAur2_renamed_fixed.fasta
        gff
        ChrAurV1_fixed.gff

        Read trimming options

        extra_trimgalore_args
        --clip_r1=1 --clip_r2=1

        Alignment options

        min_mapped_reads
        5

        Process skipping options

        skip_biotype_qc
        true

        Core Nextflow options

        configFiles
        N/A
        containerEngine
        singularity
        launchDir
        /powerplant/workspace/cfnsjm/my_files/C_auratus/transcriptomics/rnaseq
        profile
        singularity
        projectDir
        /workspace/cfnsjm/.nextflow/assets/nf-core/rnaseq
        revision
        master
        runName
        thirsty_stone
        userName
        cfnsjm
        workDir
        /powerplant/workspace/cfnsjm/my_files/C_auratus/transcriptomics/rnaseq/work